1. Field of the Art
The present invention relates to elevator group control method and apparatus.
2. Background of the Art
In elevator group control, assignment control using evaluation functions prevails in this age.
According to such control, each time a hall call occurs, numerical calculations are made for each elevator car with the use of evaluation functions in order to find an optimum elevator car to which such a call is to be assigned. The call is then assigned to the car having the largest or smallest value out of the values thus calculated. According to this method, an advanced group control may be achieved by suitably combining a variety of evaluation functions with the use of parameters.
However, conventional control systems employ constant evaluation functions and parameters. It is therefore difficult for such system to express sophisticated knowledge which experts would use to make a judgment. Accordingly, conventional methods do not always meet the requirements of diversified in-building traffic which varies from time to time.
To achieve a more advanced group control, a proposal has been made of a hall call assignment control by an expert system with the use of fuzzy inference.
In this control method, a variety of evaluation indexes relating to waiting time for a hall call, the probabilities of a long waiting time, the probability of first car arrival, etc., as well as assignment aptitude of car, are expressed in terms of fuzzy variables. Values to such variables are assigned using fuzzy sets: (1) L--(Large), (2) M--(Medium), (3) S--(Small), (4) VG--(Very Good), (5) G--(Good) and (6) VB--(Very Bad). In rule groups, suitable call-assignment methods are expressed in the IF-THEN fuzzy conditional statements. With the use of such rule groups, an optimum car may be selected and assigned based on the degree of conformance of each car for each rule. This control method is now described in more detail in the following.
Consideration is now made on a rule group including the following three rules with the use of evaluation indexes of F.sub.1 and F.sub.2 only for simplification of the description:
Rule (1)
IF F.sub.1 (j)=L,
THEN A(j)=VG PA1 IF F.sub.1 (j)=M AND F.sub.2 (j)=M, PA1 THEN A(j)=G PA1 IF F.sub.1 (j)=S OR F.sub.2 (j)=L, PA1 THEN A(j)=VB PA1 where PA1 F.sub.1L : F.sub.1 is large; PA1 F.sub.1M : F.sub.1 is medium; and PA1 F.sub.1S : F.sub.1 is small. PA1 F.sub.2L : F.sub.2 is large; PA1 F.sub.2M : F.sub.2 is medium; and PA1 F.sub.2S : F.sub.2 is small. PA1 A.sub.VG : The assignment aptitude is very good; PA1 A.sub.G : The assignment aptitude is good; and PA1 A.sub.VB : The assignment aptitude is very bad. PA1 IF F.sub.1 =L AND F.sub.2 =L; and PA1 IF F.sub.1 =L OR F.sub.2 =L. PA1 (1) a knowledge base unit storing a plurality of pre-determined rule groups to which priority orders are respectively given; PA1 (2) a rule set selecting unit for successively selecting the rule groups according to the priority orders thereof; PA1 (3) an evaluation index calculation unit for executing calculations of evaluation indexes, based on a traffic information signal, when a hall call occurs; PA1 (4) a fuzzy inference unit for obtaining the degree of conformance of each elevator car for each rule, from evaluation indexes and membership functions, and for obtaining, based on the degree of conformance thus obtained, the assignment aptitude value of each car for each rule group; and PA1 (5) an assignment aptitude evaluation unit for advancing, by a single step, the selection operation of the rule set selecting unit at the time only when there is at least one car, excluding the car whose assignment aptitude value is optimum, which has the difference in assignment aptitude value to the current rule group, from that of an optimum car, of not greater than a predetermined threshold value, and for stopping the selection operation of the rule set selecting unit when the differences in assignment aptitude values between a car whose value is optimum and that of all other cars are greater than a predetermined threshold value, thereby to provide an assignment signal for selecting the car whose assignment aptitude value is optimum, and assigning a call to the car.
Rule (2)
Rule (3)
F.sub.1 (j): Value of the evaluation index F.sub.1 when a call is assigned to elevator car j (fuzzy variable) PA2 F.sub.2 (j): Value of the evaluation index F.sub.2 when a call is assigned to elevator car j (fuzzy variable) PA2 A(j): Assignment aptitude of the elevator car j (fuzzy variable) PA2 L: Large PA2 M: Medium PA2 S: Small PA2 VG: Very good PA2 G: Good PA2 VB: Very bad PA2 AND: Logical product PA2 OR: Logical sum
Accordingly, the Rule (1) represents that, when a call is assigned to elevator car j, the assignment aptitude of car j is very good if F.sub.1 is large. The Rule (2) represents that, when a call is assigned to car j, the assignment aptitude of car j is good if F.sub.1 is medium and F.sub.2 is medium. The Rule (3) represents that, when a call is assigned to elevator car j, the assignment aptitude of car j is very bad if F.sub.1 is small or F.sub.2 is large.
First, the degree of conformance for each rule is obtained for each car. Based on the values thus obtained, a car with the optimum assignment aptitude is selected. The degree of conformance of each car for each rule is obtained from fuzzy variables corresponding to each evaluation index with the use of membership functions shown in FIG. 3.
FIG. 3 (a) shows membership functions representing the following fuzzy sets:
Likewise, FIG. 3 (b) shows membership functions representing the following fuzzy sets:
FIG. 3 (c) shows membership functions representing the following fuzzy sets:
FIG. 4 shows procedures of obtaining the assignment aptitude value of an elevator car for the above-stated rules.
For example, when Rule (1) is applied to elevator car j, the degree of conformance thereof is calculated in the following manner.
First, F.sub.1 (j), or F.sub.1 where a call is tentatively assigned to car j, is calculated. Then, the attribute degree of the F.sub.1 (j) thus calculated to the fuzzy set representing that F.sub.1 is great, is obtained from the membership function F.sub.1L. As shown in FIG. 4 (a), this degree is 0.9 in this example. Accordingly, the assignment aptitude degree of car j for Rule (1) is obtained by multiplying the function A.sub.VG by 0.9, as shown in FIG. 4 (b).
Likewise, the degree of conformance of car j for Rule (2) is obtained in the following manner.
Based on the logical product of (i) the attribute degree of F.sub.1 (j) to the fuzzy set representing that F.sub.1 is medium, i.e., 0.9 as shown in FIG. 4 (c), and (ii) the attribute degree of F.sub.2 (j) to the fuzzy set representing that F.sub.2 is medium, i.e., 0.4 as shown in FIG. 4 (d), the smaller value or 0.4 is selected as the degree of conformance. Accordingly, the assignment aptitude degree of car j for Rule (2) is obtained by multiplying the function A.sub.G by 0.4, as shown in FIG. 4 (e).
Likewise, the degree of conformance of car j for Rule (3) is obtained in the following manner.
Based on the logical sum of (i) the attribute degree of F.sub.1 (j) to the fuzzy set representing that F.sub.1 is small, i.e., 0.3 as shown as shown in FIG. 4 (f), or (ii) the attribute degree of F.sub.2 (j) to the fuzzy set representing that F.sub.2 is large, i.e., 0.8 as shown in FIG. 4 (g), the greater value or 0.8 is selected as the degree of conformance. Accordingly, the assignment aptitude degree of car j for Rule (3) is obtained by multiplying the function A.sub.VG by 0.8, as shown in FIG. 4 (h).
As shown in FIG. 4 (i), the logical sum of FIG. 4 (b), (e), and (h) represents the assignment aptitude degree of car j for Rules (1) to (3), and the center of gravity of the graph shown in FIG. 4 (i) represents the assignment aptitude value of car j to the abovestated rules.
According to the above procedures, the assignment aptitude values of all elevator cars to the rules are obtained. The call is assigned to the car having the best assignment aptitude value (in this example, the car whose center of gravity of the graph in FIG. 4 (i) is located at the leftmost position).
According to the call assignment method using the fuzzy inference, the knowledge of experts may be readily incorporated in the control system by suitably setting the membership functions, the contents of the rules and the number of rules. This enables a delicate group control of elevators conforming to requirements of the building.
However, such a call assignment method using the fuzzy inference presents following problems.
For example, when two sets that F.sub.1 is large and F.sub.2 is large, are used as conditions, the rule may be expressed in the following two manners:
When the rule is expressed with the use of AND i.e., logical product, the same evaluation is made for both cases where F.sub.1 is large and F.sub.2 is small and where F.sub.1 and F.sub.2 are both small. On the other hand, when the rule is expressed with the use of OR i.e., logical sum, the same evaluation is made for both cases where F.sub.1 is large and F.sub.2 is small and where F.sub.1 and F.sub.2 are both large. Thus, there is no difference in evaluation between these cases.
To avoid such a problem, it is required to prepare additional rules of other combinations of F.sub.1 with F.sub.2. However, increase in the number of evaluation indexes results in increase in the combinations thereof, and it is difficult to express, as rules, all necessary combinations of all evaluation indexes. Further, a failure to write necessary rules may be involved. If a number of rules are prepared, this produces rules for which no evaluation would be required dependent on the status of calls and elevator cars. Even in such case, calculations are made for all rules, resulting in a waste of time.